Search Results (650)

Pathogenic fungi and their synergistic interactions with bark beetles, leading to vascular dysfunction, physiological stress, and eventual tree mortality, increasingly threaten forest ecosystems. Because fungal colonization often precedes visible macroscopic symptoms, early detection remains a critical yet challenging task in forest health monitoring. This study proposes a real-time deep learning-based object detection framework for identifying harmful fungi in proximity to host trees to support early intervention strategies. A custom dataset comprising 8900 images was constructed to represent two classes: Healthy and Unhealthy trees, where fungal presence is detected either directly on the tree or within its immediate ecological vicinity (e.g., near root systems). A fine-tuned YOLOv11 detection architecture is developed and augmented with a squeeze-and-excitation (SE)-like attention mechanism to enhance texture-sensitive feature representation. The model is trained and evaluated using precision, recall, F1-score and mean Average Precision (mAP). Experimental results demonstrate an overall mAP@0.5 of 0.825, with class-wise average precision values of 0.926 (Healthy) and 0.724 (Unhealthy). The Healthy class achieved classification accuracy of 0.92, while 0.71 of Unhealthy instances were correctly detected. F1-Confidence and recall-Confidence metrics indicate that optimal operational performance occurs within a confidence threshold range of 0.30–0.35, balancing false positives and false negatives. Despite the approximately balanced class distribution (50.6% Healthy and 49.4% Unhealthy), detection performance for the Unhealthy class was comparatively lower because of its greater intra-class variability, heterogeneous fungal appearance, and subtle visual manifestations. Findings demonstrate the feasibility of deploying real-time object detection models for early-stage fungal surveillance and highlight the importance of confidence calibration for operational disease monitoring systems. Full article

The integration of digital therapeutics (DTx), wearable electronic devices, and artificial intelligence (AI) represents a significant advancement in personalized healthcare. The primary purpose of this structured narrative review is to evaluate the convergence of these technologies, providing a consolidated framework that bridges the gap between raw biometric data acquisition and actionable, AI-driven clinical insights. This paper synthesizes the latest literature on the intersection of mobile health (mHealth), machine learning (ML), and physiological tracking, with a primary focus on heart rate variability (HRV) and associated biochemical markers, such as cortisol, salivary alpha-amylase, and interleukins. Instead of viewing wearable outputs simply as raw data, we critically evaluate the technical verification and clinical validation required to define them as true “digital biomarkers.” By evaluating multimodal sensor technologies and advanced predictive algorithms, this paper outlines the clinical utility of digital biomarkers in diagnosing and proactively managing cardiovascular, neurological, metabolic, and psychiatric conditions, noting classification accuracies frequently exceeding 85% in controlled settings. However, we strongly caution that internally validated performance in controlled settings does not inherently demonstrate external clinical utility. The clinical relevance of this study lies in its holistic approach to identifying how continuous monitoring can broaden healthcare accessibility while improving precision medicine. Furthermore, it deeply addresses the technical challenges of highly variable ambulatory data quality, the necessity for robust artifact reduction (e.g., via LSTM and GAN architectures), and the limitations of small, homogeneous training datasets. We highlight the essential need for demographic-aware algorithmic models, external validation, and decentralized privacy-preserving models (e.g., federated learning) in diverse populations to ensure the safe, equitable clinical translation of DTx, mHealth, ML, and AI technologies. Full article

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust, debris, occlusion, scale variation, and abrupt scene transitions. This paper presents E’CHIT (Edge-Oriented Colour Histogram Instance-Guided Tracking), a deployment-oriented, operator-centric UAV tracking framework for real-world disaster-response applications. Its primary scientific contribution is an identity-stabilised, detector-assisted tracking methodology. YOLOv8-seg proposals trained on D’RespNeT initialise and refresh tracks; a Custom-RE3 recurrent module propagates target states through short detector dropouts; and a lightweight EOMC verifier, based on edge orientation, mean colour, and shape consistency, determines whether tracks should be accepted, refreshed, or reacquired. A scene-cut watchdog that combines luminance mean absolute difference (MAD) with HSV histogram divergence prevents stale identities from carrying over after hard edits or sudden feed changes. Custom-RE3 is the continuation module implemented and evaluated in this study. The surrounding E’CHIT wrapper follows an initialise–reseed–verify–reset cycle and is tracker-adaptable at the software-interface level: another compatible SOT or MOT continuation module can be integrated through adapter modifications, state and bounding-box conversion, and method-specific retuning, followed by independent validation. All reported quantitative results therefore apply to the Custom-RE3 implementation. D’RespNeT, the optional reinforcement learning (RL) warm start, the HUD, and the deployment stack support this central tracking contribution. D’RespNeT provides 28 polygon-annotated SAR classes. An author-developed PPO/SAC script is used only during offline detector training. In the reported runs, it produces different early optimisation trajectories for selected difficult or under-represented classes, while the default supervised schedule remains the strongest final global mAP reference. No RL policy runs during deployment; the detector architecture, parameter count, and inference graph remain unchanged. Evaluation on D’RespNeT and authentic disaster-response UAV footage shows that E’CHIT increases Success@IoU ≥ 0.5 from 0.62 to 0.79, reduces identity switches by approximately 71%, and maintains real-time 1080p performance, achieving 164–330 FPS for single-target tracking and 24–100+ FPS for end-to-end multi-target operation on an RTX-class GPU using FP16. The VOT2014, NT-VOT211, and VOTS2024 figures reproduce historical result spaces reported in the literature and include a clearly labelled, non-official E’CHIT operating-point marker solely for context. This marker was not produced using the corresponding official datasets, toolkits, reset rules, or submission routes; it is excluded from the primary quantitative claims and must not be interpreted as a leaderboard rank or a protocol-identical comparison. Overall, the system demonstrates how identity-stable UAV tracks can provide actionable operator cues for target monitoring, entry-point assessment, and UAV–UGV/ground-team coordination in cluttered disaster scenes. Full article

Accurate characterization of rock anisotropy is crucial for underground engineering stability assessment. In this study, multi-directional drilling tests were performed on sandy mudstone and argillaceous sandstone, with real-time monitoring of feed force (F), torque (M), rotational speed (n), power (P), drilling velocity (v), and depth (h). Drilling-derived apparent friction angles (φ) in different directions were estimated using a force-equilibrium-based model. Based on drill bit geometry, an energy balance model for hollow drilling was developed, and a unit grinding energy (η_e) was derived. A preliminary drilling-derived anisotropy index ($B_{\phi -{\eta }_e}$), based on the coefficient of variation (CV) and integrating φ and η_e, was proposed. Results show a strong linear correlation between thrust force and torque, both exhibiting a two-stage increase with drilling depth. For the tested drilling orientations, the anisotropy determined using the proposed method decreases in the following order: sandy mudstone 1, sandy mudstone 2, argillaceous sandstone 2, and argillaceous sandstone 1. The proposed index provides a preliminary basis for evaluating directional variations in rock anisotropy. Full article

Radiation-free treatment for early-stage classic Hodgkin lymphoma (eHL) has been shown to be less effective than standard combined-modality treatment (CMT; chemotherapy plus involved-node radiotherapy (INRT)), which achieves long-term disease control of 94–95%. Approximately 70% of patients can be cured with chemotherapy alone, whereas about 5% fail CMT. Identifying patients who can safely receive chemotherapy alone and those requiring intensified CMT could enable a risk-adapted treatment strategy. The RAFTING trial (NCT04866654; EudraCT 2020-002382-33) is an international, prospective, phase 2, non-inferiority study enrolling patients 18–70 years, stage I–IIA eHL without bulky disease, B symptoms, or extranodal involvement. Low-risk (LR) patients are defined by total metabolic tumor volume (TMTV) <84 mL and negative PET-2. Those with at least one modified EORTC (mEORTC) risk factor, in which bulky disease is replaced by a large nodal mass (5–10 cm), receive four ABVD cycles, while those without risk factors receive two ABVD cycles alone. High-risk (HR) patients, defined by TMTV ≥84 mL and/or positive PET-2, receive “triple therapy”: 4 ABVD cycles, INRT (20/30 Gy), and nivolumab (240 mg q2w, ≤doses). LR patients are monitored using cfDNA. Limited relapse is treated with INRT (36 Gy) and nivolumab. The RAFTING trial is the first prospective eHL study to personalize treatment using TMTV and PET-2. It aims to omit radiotherapy in LR patients, intensify treatment in HR patients, and spare relapsed LR patients high-dose chemotherapy and autologous transplantation. CfDNA is being evaluated as a relapse marker. Despite the protocol’s complexity, this study exemplifies personalized medicine and could transform treatment practices. Full article

Background/Objective: Persistent infection with human papillomavirus type 16 (HPV-16) is the principal cause of cervical high-grade squamous intraepithelial lesions (cHSIL) and cervical cancer, yet the established treatments remain limited to ablative or excisional procedures that carry reproductive risk and do not eliminate the underlying infection. We report the preclinical development of ARV-2001, a messenger RNA (mRNA)–lipid nanoparticle (LNP) immunotherapeutic encoding mutated, non-oncogenic HPV-16 E6 and E7 fused to a SARS-CoV-2 spike S2 subdomain enriched in human CD4 helper epitopes, formulated in a novel cholesterol-derived ionizable lipid (ARV-T1). Methods: Interactions of ARV-2001-expressed antigens with p53 and retinoblastoma (Rb) were evaluated in human cervical carcinoma cell line C33A, in lentiviral constructs in primary human keratinocytes, and in soft-agar colony-formation assays. ARV-2001 was administrated by intramuscular (IM) or intradermal (ID) injection in naive mice or in the TC-1 tumor models. Tumor size and survival were monitored over time and tumor-infiltrated lymphocytes were characterized by flow cytometry. Intracellular cytokine staining and Elispot were used to evaluate immunogenicity. Results: In vitro, the mutated E6/E7–S2 antigen lost the ability to degrade p53, to deregulate the retinoblastoma (Rb) pathway, and to support anchorage-independent growth, suggesting abrogation of oncogenic activity. The S2 domain and imiquimod administration each augmented antitumor activity and intratumoral CD8⁺ T-cell infiltration while reducing myeloid-derived suppressor cells in the syngeneic HPV-16 E6/E7 TC-1 tumor models. In addition, ID administration of ARV-2001 into TC-1 tumor-bearing mice was superior to IM administration in terms of both tumor growth inhibition and survival. ID vaccination with ARV-2001 in mice consistently elicited a more potent E6/E7-specific T-cell response than the same dose given IM. Dose-escalation studies showed a dose-dependent T cell response against E6/E7 in ID-injected mice. Conclusions: This study supports future human evaluation of intradermally administrated ARV-2001 for treatment of HPV-16+ cHSIL in clinical trials. Full article

Background/Objectives: Because human prostate cancer (PCa) typically exhibits slow tumor growth, establishing reliable PCa tumor models is often time-consuming and unpredictable, thereby limiting the efficiency of preclinical theranostic research. To overcome this limitation, this study employed a non-viral PiggyBac transposon system to introduce triple-reporter genes into PSMA-expressing C4-2 cells, generating orthotopic and subcutaneous xenograft models that allow noninvasive, real-time monitoring of PCa progression and treatment response. Methods: Reporter-engineered C4-2 3R cells were generated by co-transfecting constructs encoding the reporter cassette and PB transposase, followed by enrichment through fluorescence microscopy and fluorescence-activated cell sorting (FACS) and implantation orthotopically or subcutaneously into mice. Tumor growth and treatment response to a single 2 Gy X-ray dose followed by 14.8 MBq of ¹⁷⁷Lu-PSMA-617, or to each monotherapy, were monitored weekly using an IVIS imaging system. Imaging findings were validated by tumor dissection and hematoxylin and eosin (H&E) staining, while PSMA expression was assessed by Western blotting and ¹⁸F-PSMA-1007 PET/CT. Results: C4-2 3R cells stably expressed the triple reporter genes (mRFP, luc2, and HSV1-tk), generating detectable orthotopic bioluminescence within one week and persisting for at least five weeks. In contrast, subcutaneous implantation generated only transient luc2 signals with no tumor formation. X-ray exposure did not increase total PSMA levels but induced the redistribution of PSMA to the cell membrane. Combined external beam radiotherapy (EBRT) and ¹⁷⁷Lu-PSMA-617 treatment produced higher ¹⁸F-PSMA-1007 uptake and stronger tumor suppression, with minimal residual tumor mass, as compared to single-treatment or control groups. Conclusions: This preliminary investigation suggests that the C4-2 3R model provides a practical and trackable tool for investigating slow-growing PCa tumors and evaluating PSMA-targeted therapies, either alone or in combination with EBRT. Full article

Peste des petits ruminants virus (PPRV) causes a highly fatal disease that severely impacts small ruminant production and global food security. This study aimed to develop a rapid, user-friendly colloidal gold immunochromatographic test strip for detecting PPRV-specific antibodies using a double-antigen sandwich format. Bioinformatic analysis using DNASTAR Protean was performed to predict candidate antigenic regions in the PPRV H and N proteins. Three predicted candidate regions from each protein were selected and incorporated into the design of the recombinant fusion antigen PPRV-H₃N₃EP. The recombinant antigen was expressed in E. coli, purified, and refolded to obtain a final concentration of 8.52 mg/mL. The strip was assembled with colloidal gold-labeled fusion protein as the detection probe and unlabeled protein coated on the test line, plus an independent mouse IgG/goat anti-mouse IgG control system. Performance evaluation showed that the results were readable within 10–15 min. The strip showed satisfactory analytical sensitivity and cross-reactivity performance, consistent qualitative results in within-batch repeatability testing, and preliminary short-term storage stability. In a comparative evaluation using sheep serum samples and a commercial competitive enzyme-linked immunosorbent assay (ELISA) kit, the overall agreement reached 97.9%. Collectively, the constructed PPRV-H₃N₃EP antigen enabled a simple, rapid, and reliable strip assay suitable for field detection of PPRV antibodies and post-vaccination monitoring, while also providing a methodological reference for developing antibody tests for other pathogens. Full article

Endocrine-disrupting compounds (EDCs) are frequently detected in wastewater and aquatic environments because of their incomplete removal in conventional wastewater treatment plants. Evaluating alternative treatment technologies, including microalgae-based systems, requires analytical methods suitable for low-volume samples. In this work, a solid-phase extraction (SPE) procedure coupled with high-performance liquid chromatography with diode array detection (HPLC-DAD) was developed for the simultaneous determination of methylparaben (MeP), propylparaben (PrP), butylparaben (BuP), benzophenone (BP), bisphenol A (BPA) and estrone (E). The method combines relatively low sample volume and limited eluent consumption with the analysis of six chemically diverse EDCs in biomass-containing samples using accessible HPLC-DAD instrumentation. Sorbent mass, sample volume, elution volume, and pH were evaluated experimentally, with Random Forest used for exploratory multivariable interpretation. Selected conditions were a 500 mg cartridge, 30 mL of sample volume at pH 7 and 2 mL methanol elution volume. The method showed satisfactory linearity (R² ≥ 0.98), limits of quantification between 1.67 and 6.67 ppb, and stable recoveries across the evaluated concentration range. Applicability was demonstrated in a Scenedesmus sp.-based system. AGREEprep assessment yielded a score of 0.34. The developed SPE method provides a low-volume approach for monitoring EDCs in microalgae-based wastewater treatment systems. Full article

Temperature–time profiles obtained through thermistor-based monitoring provide a rich but noise-sensitive source of information for estimating kinetic and thermal parameters in exothermic batch reactions. Conventional workflows typically combine deterministic smoothing with numerical differentiation, an approach that can amplify measurement noise and fail to propagate preprocessing uncertainty into the resulting reaction-rate and parameter estimates. To address these limitations, this study presents an uncertainty-aware Bayesian machine-learning framework that integrates scalable random-Fourier-feature Gaussian-process (RFF–GP) smoothing, analytical differentiation, temperature-derived apparent conversion, Bayesian parameter inference, posterior validation, predictive calibration, model comparison, ablation, sensitivity analysis, probabilistic benchmarking, simulation of thermal nonideality, and endpoint diagnostics. The framework was applied to $379,631$ cleaned thermistor observations. The production RFF–GP achieved a validation root-mean-square error of $0.04805\mathrm{K}$, yielding a stable latent temperature trajectory and an uncertainty-aware estimate of $dT/dt$. On a smaller matched subset, exact Gaussian-process regression achieved the highest predictive accuracy and the best probabilistic scores, whereas the RFF–GP reduced central-processing-unit runtime by approximately $4.1$-fold and remained applicable to the larger production fit. A Monte Carlo dropout neural comparator produced larger prediction errors and substantially wider predictive intervals. Six apparent thermokinetic structures were evaluated using mean-field variational inference, after which the nth-order and autocatalytic structures were validated using the No-U-Turn Sampler (NUTS). Under mean-field variational inference, the apparent autocatalytic structure achieved the lowest point estimate of the widely applicable information criterion (WAIC), the lowest derivative-domain error, and the lowest full-profile temperature-reconstruction root-mean-square error of $0.2920\mathrm{K}$. Its posterior obtained using NUTS yielded $E_a=40.98\mathrm{kJ}{\mathrm{mol}}^{-1}$, $k_{\mathrm{ref}}=0.005815{\min }^{-1}$, $\Delta T_{\mathrm{ad}}=56.11\mathrm{K}$, $m=0.1694$, and $n=1.0784$. The sampling diagnostics indicated satisfactory convergence, large effective sample sizes, and no divergent transitions. Although the MFVI posterior means and NUTS posterior medians were similar, variational inference produced narrower uncertainty intervals for several correlated parameters. Moving-block bootstrap intervals did not establish a decisive separation in WAIC among the leading structures. Expanded sensitivity, ablation, imperfect-insulation simulation, and endpoint-holdout analyses further showed that the apparent parameter estimates were sensitive to optimization, thermal nonideality, sensor response, and Gaussian-process boundary behavior. The autocatalytic formulation should therefore be interpreted as the best-performing apparent structure among the candidates tested rather than as evidence of a unique chemical mechanism. Overall, the framework extracted physically plausible apparent thermokinetic information from noisy temperature-only measurements while explicitly quantifying uncertainty arising from prediction, parameter estimation, model form, computation, thermal nonideality, and boundary behavior. Full article

Background: Autophagy is a process that diversely impacts the stages of both tumor initiation and progression. Elucidating the molecular mechanisms underlying autophagy and its role in tumorigenesis is a key component of anticancer strategies in both prostate cancer and other malignancies. Because advanced prostate cancer frequently exploits enhanced autophagy as a defense mechanism against therapy-induced stress (e.g., from abiraterone), the pharmacological modulation of miRNA levels presents a tremendous opportunity to block the tumor’s escape route and overcome drug resistance. Methods: A comprehensive literature review was conducted to evaluate the molecular pathways determining cancer cell survival and death. The analysis focused on the dual nature of autophagy (functioning as a ‘double-edged sword’) within the tumor microenvironment, microRNA (miRNA) regulatory networks, and the efficacy of synergistic therapeutic strategies in overcoming treatment resistance. Results: The primary focus of this paper is the dual and complex role of autophagy, which serves, on the one hand, as a cellular protective shield against metabolic stress—thereby facilitating metastasis—and, on the other hand, as a potential pathway leading to autophagic cell death. The progression of this crucial process is regulated by intricate interactions (crosstalk) with apoptotic pathways, mediated by Bcl-2 family proteins, key kinases (such as mTOR, JNK, and DAPK), and transcription factors, such as p53. Furthermore, the autophagic machinery is precisely regulated by specific miRNA molecules (e.g., miR-21, miR-141, and miR-375). These not only act as crucial intracellular modulators of autophagy but also serve as promising circulating biomarkers, enabling the monitoring of this process’s activity throughout disease progression. Conclusions: Autophagy, and in particular its modulation via miRNA signaling networks, represents a major and highly promising translational target. By directly impairing this autophagic survival mechanism, ‘double-hit’ combination therapies—integrating autophagy inhibitors (such as hydroxychloroquine or VPS34 inhibitors) with standard antiandrogen or cytotoxic agents—demonstrate promising preclinical potential in overcoming treatment resistance and favorably modulating the immune microenvironment in advanced prostate cancer. Full article

Purpose: Upper extremity motor impairments following stroke substantially limit independence in daily living. Hand-tracking virtual reality (VR) rehabilitation systems may support intensive task-oriented practice while enhancing motivation; however, evidence integrating objective kinematic indicators with usability and patient-reported outcomes for controller-free consumer-grade VR remains limited. The primary aim of this single-arm pilot study was to evaluate the feasibility, safety (tolerability), and usability of a hand-tracking VR rehabilitation system delivered as an adjunct to conventional physiotherapy in individuals with stroke; describing its preliminary in-game kinematic profile and exploring participants’ experiences through open-ended feedback were secondary aims. The study was explicitly not designed or powered to test clinical efficacy. Materials and Methods: Ten individuals with stroke completed a 20-session (8-week) single-arm pilot intervention; all participants concurrently received standard hospital-based physiotherapy (median 3 sessions/week, ∼45 min/session). Early-phase (sessions 1–5) and late-phase (sessions 16–20) within-subject performance were compared using the Wilcoxon signed-rank test with Holm–Bonferroni correction across four pre-specified primary outcomes. Movement smoothness was assessed using the Spectral Arc Length (SPARC), usability was evaluated using the System Usability Scale (SUS), and cybersickness was monitored with the Simulator Sickness Questionnaire (SSQ). Results: Sixteen patients were screened, of whom fourteen started the intervention and ten completed the 8-week per-protocol program (intervention completion rate, 10/14 = 71.4%; per-protocol session adherence among the ten completers, 200/200 = 100%; no SSQ-defined adverse events). Within-subject comparisons showed a $+10.6$-point increase in success rate (adjusted $p=0.020$), a $+0.10$ m/s increase in mean movement speed (adjusted $p=0.020$), a $-341$ ms reduction in pause duration (adjusted $p=0.022$), and a $+27.0$-point Hodges–Lehmann median-difference increase in SS-QOL (95% CI $14.0$–$41.0$; participant-level median $\mathsf{\Delta }$$+18.5$; adjusted $p=0.020$). The mean SUS score was $78.5\pm 5.4$, indicating “good” usability. Thematic analysis of post-intervention open-ended feedback identified three themes—motivation and engagement, the value of feedback, and design and comfort suggestions—that converged with the high adherence and good usability ratings. Correlations between VR-derived kinematic change and clinical change were non-significant trends ($p\ge 0.12$). Conclusions: A controller-free hand-tracking VR system was found to be feasible, well tolerated, and rated as having good usability when delivered as an adjunct to conventional therapy. Because the study used a single-arm design, included only ten participants, and did not control for the confounding effect of concurrent conventional physiotherapy or natural recovery, the observed within-subject changes cannot be causally attributed to the VR intervention and should be interpreted as exploratory feasibility signals. Adequately powered randomized controlled trials with stroke-specific clinical scales (e.g., FMA-UE, ARAT) are required before clinical efficacy can be claimed. Full article

Active-layer thickness (ALT) integrates atmospheric forcing with local surface and subsurface hydrothermal conditions, but the incremental predictive value of precipitation after temperature and site memory have been considered to remain uncertain. We combined annual ALT observations from 54 boreholes along the Xidatan–Anduo section of the Qinghai–Tibet Engineering Corridor (2001–2020) with monthly Third Pole Meteorological Forcing Dataset data (1986–2020). A rolling-origin design evaluated nine held-out years (2012–2020) after accounting for site fixed effects, a quadratic temporal trend, previous-year ALT, and an air-temperature lag selected from each training set. Because the principal predictors include target-year precipitation, the analysis represents retrospective or end-of-season annual estimation rather than a lead-time forecast. Among 16 seasonal–lag candidates, the post-screening best fixed model used June–August precipitation averaged over the target year and the three preceding years; it reduced RMSE from 0.203491 to 0.199312 m ($\mathsf{\Delta }RMSE$ = 0.004179 m; 2.05%). A selection-aware maximum statistic remained supported under site-specific circular shifts (p = 0.001), whereas a common-shift sensitivity that preserved synchronous cross-site climate structure was inconclusive. A pipeline that repeated precipitation-window selection using training data only produced a smaller gain ($\mathsf{\Delta }RMSE$ = 0.001645 m; 0.81%; p = 0.004). Ridge regression and random forest showed similarly small paired gains, but histogram gradient boosting did not. A continuous mean annual ground temperature (MAGT) interaction did not improve external performance, strict spatial-block × time holdout showed no transferable precipitation gain, and a predictor restricted to precipitation from years t − 1 to t − 4 did not improve RMSE. The fixed-window gain was substantially smaller than the source-reported ±0.05 m uncertainty of an individual ALT estimate. Seasonal precipitation should therefore be treated as an auxiliary covariate for retrospective assessment at monitored sites, not as a stand-alone forecast, an ungauged-site hazard model, or evidence of maintenance-cost savings. Full article

Cotton boll opening is an important late-season indicator for maturity assessment, defoliation timing, and harvest planning, but field-level monitoring remains challenging because visible lint progression varies spatially and temporally. This study evaluates whether UAV-derived cotton visible lint percentage, aggregated at the Sentinel-2 10 m grid scale, can be estimated using Sentinel-2 spectral bands, vegetation indices (VIs), and accumulated growing degree days (AGDD) as phenological predictors. UAV multispectral imagery was used to derive visible lint percentage through red-band thresholding and segmentation within canopy masks. The UAV-derived visible lint information was summarized within fixed Sentinel-2 10 m grid cells to generate satellite-compatible response labels. Four supervised regression models, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), k-Nearest Neighbors (kNN), and Neural Network/Multilayer Perceptron (NNET/MLP), were evaluated using raw and transformed target formulations. Raw visible lint percentage produced relatively high explanatory power for tree-based models, with R² values of 0.73 for both XGBoost and RF. However, the target distribution was strongly right-skewed and dominated by low visible lint values, with a mean PCTOPEN of 4.05%Open, motivating the evaluation of target transformations to reduce target skewness while assessing their impact on predictive performance. On the original PCTOPEN scale, the raw target produced RMSE = 4.38%Open points, MAE = 2.31%Open points, and MedianAE = 0.68%Open points. The square-root transformation provided the strongest overall predictive performance, maintaining R² = 0.73 and RMSE = 4.38%Open points while reducing MAE to 2.14%Open points and MedianAE to 0.41%Open points. Stronger transformations further reduced the typical prediction errors, with MedianAE = 0.35, 0.33, and 0.31%Open points for the cube-root, fourth-root, and fifth-root transformations, respectively. However, these improvements were accompanied by progressively lower R² values (0.72, 0.71, and 0.69) and higher RMSE values (4.48, 4.58, and 4.68%Open points), indicating that stronger transformations reduced typical prediction errors at the expense of overall predictive performance. These results indicate that UAV-derived visible lint percentage can be linked with Sentinel-2 observations for satellite-scale regression modeling, but prediction uncertainty remains influenced by target skewness, mixed 10 m pixels, canopy obstruction, and limited UAV acquisition density. Additional UAV–Sentinel-2-aligned acquisitions, supporting multi-field observations, and broader validation across fields, seasons, cultivars, and production environments are needed to improve robustness and support operational cotton boll-opening tracking applications. Full article

This work is an extension of our previous work on UV-assisted green synthesis of silver nanoparticles (AgNPs) from water hyacinth leaf extract, which is focused on using their unique biogenic capping layer to mitigate matrix interference in clinical diagnostics. The synthesized AgNPs were evaluated for uric acid (UA) detection and used to fabricate a plasmonic colorimetric biosensing platform. The results show that the biosynthesized AgNPs effectively act as optical signal transducers in an uricase-based enzymatic system, in which the natural capping shield provides excellent electrosteric protection, providing excellent colloidal stability without non-specific aggregation in complex matrices. A linear correlation between absorbance and UA concentration was observed in the range of 100–500 µM (R² = 0.99). The limit of detection (LOD) calculated by the 3σ/slope approach was 25.89 µM. The sensor showed good repeatability with a relative standard deviation (RSD) below 5%, and stability studies showed that the AgNPs retained more than 90% of their initial response after 14 days. Recovery studies in spiked human serum showed satisfactory accuracy (96.3–103.4%), confirming a high tolerance to endogenous interfering species (e.g., ascorbic acid and glutathione) without pre-purification steps. Importantly, the working range covers clinically relevant uric acid concentrations found in human serum. The obtained results point to the potential of waste-derived green-stabilized AgNPs as a robust plasmonic-based colorimetric biosensing platform for the clinical monitoring of uric acid, successfully combining ecological valorization with high-performance, matrix-tolerant diagnostics. Full article